/*
 * Copyright 2010-2012 Susanta Tewari. <freecode4susant@users.sourceforge.net>
 *
 * This program is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

package bd.org.apache.commons.math.stat.descriptive.moment;

import bd.org.apache.commons.math.exception.NullArgumentException;
import bd.org.apache.commons.math.stat.descriptive.AbstractStorelessUnivariateStatistic;
import bd.org.apache.commons.math.util.FastMath;
import bd.org.apache.commons.math.util.MathUtils;

import java.io.Serializable;

/**
 * Computes the sample standard deviation.  The standard deviation
 * is the positive square root of the variance.  This implementation wraps a
 * {@link Variance} instance.  The <code>isBiasCorrected</code> property of the
 * wrapped Variance instance is exposed, so that this class can be used to
 * compute both the "sample standard deviation" (the square root of the
 * bias-corrected "sample variance") or the "population standard deviation"
 * (the square root of the non-bias-corrected "population variance"). See
 * {@link Variance} for more information.
 * <p>
 * <strong>Note that this implementation is not synchronized.</strong> If
 * multiple threads access an instance of this class concurrently, and at least
 * one of the threads invokes the <code>increment()</code> or
 * <code>clear()</code> method, it must be synchronized externally.</p>
 *
 * @version $Id: StandardDeviation.java 1244107 2012-02-14 16:17:55Z erans $
 */
public class StandardDeviation extends AbstractStorelessUnivariateStatistic
        implements Serializable {

    /** Serializable version identifier */
    private static final long serialVersionUID = 5728716329662425188L;

    /** Wrapped Variance instance */
    private Variance variance = null;

    /**
     * Constructs a StandardDeviation.  Sets the underlying {@link Variance}
     * instance's <code>isBiasCorrected</code> property to true.
     */
    public StandardDeviation() {

        variance = new Variance();
    }

    /**
     * Contructs a StandardDeviation with the specified value for the
     * <code>isBiasCorrected</code> property.  If this property is set to
     * <code>true</code>, the {@link Variance} used in computing results will
     * use the bias-corrected, or "sample" formula.  See {@link Variance} for
     * details.
     *
     * @param isBiasCorrected  whether or not the variance computation will use
     * the bias-corrected formula
     */
    public StandardDeviation(boolean isBiasCorrected) {

        variance = new Variance(isBiasCorrected);
    }

    /**
     * Constructs a StandardDeviation from an external second moment.
     *
     * @param m2 the external moment
     */
    public StandardDeviation(final SecondMoment m2) {

        variance = new Variance(m2);
    }

    /**
     * Copy constructor, creates a new {@code StandardDeviation} identical
     * to the {@code original}
     *
     * @param original the {@code StandardDeviation} instance to copy
     */
    public StandardDeviation(StandardDeviation original) {

        copy(original, this);
    }

    /**
     * Contructs a StandardDeviation with the specified value for the
     * <code>isBiasCorrected</code> property and the supplied external moment.
     * If <code>isBiasCorrected</code> is set to <code>true</code>, the
     * {@link Variance} used in computing results will use the bias-corrected,
     * or "sample" formula.  See {@link Variance} for details.
     *
     * @param isBiasCorrected  whether or not the variance computation will use
     * the bias-corrected formula
     * @param m2 the external moment
     */
    public StandardDeviation(boolean isBiasCorrected, SecondMoment m2) {

        variance = new Variance(isBiasCorrected, m2);
    }

    /**
     * {@inheritDoc}
     */
    @Override
    public void increment(final double d) {

        variance.increment(d);
    }


    /**
     * {@inheritDoc}
     */
    @Override
    public long getN() {

        return variance.getN();
    }


    /**
     * {@inheritDoc}
     */
    @Override
    public double getResult() {

        return FastMath.sqrt(variance.getResult());
    }


    /**
     * {@inheritDoc}
     */
    @Override
    public void clear() {

        variance.clear();
    }


    /**
     * Returns the Standard Deviation of the entries in the input array, or
     * <code>Double.NaN</code> if the array is empty.
     * <p>
     * Returns 0 for a single-value (i.e. length = 1) sample.</p>
     * <p>
     * Throws <code>IllegalArgumentException</code> if the array is null.</p>
     * <p>
     * Does not change the internal state of the statistic.</p>
     *
     * @param values the input array
     * @return the standard deviation of the values or Double.NaN if length = 0
     */
    @Override
    public double evaluate(final double[] values) {

        return FastMath.sqrt(variance.evaluate(values));
    }


    /**
     * Returns the Standard Deviation of the entries in the specified portion of
     * the input array, or <code>Double.NaN</code> if the designated subarray
     * is empty.
     * <p>
     * Returns 0 for a single-value (i.e. length = 1) sample. </p>
     * <p>
     * Throws <code>IllegalArgumentException</code> if the array is null.</p>
     * <p>
     * Does not change the internal state of the statistic.</p>
     *
     * @param values the input array
     * @param begin index of the first array element to include
     * @param length the number of elements to include
     * @return the standard deviation of the values or Double.NaN if length = 0
     */
    @Override
    public double evaluate(final double[] values, final int begin, final int length) {

        return FastMath.sqrt(variance.evaluate(values, begin, length));
    }


    /**
     * Returns the Standard Deviation of the entries in the specified portion of
     * the input array, using the precomputed mean value.  Returns
     * <code>Double.NaN</code> if the designated subarray is empty.
     * <p>
     * Returns 0 for a single-value (i.e. length = 1) sample.</p>
     * <p>
     * The formula used assumes that the supplied mean value is the arithmetic
     * mean of the sample data, not a known population parameter.  This method
     * is supplied only to save computation when the mean has already been
     * computed.</p>
     * <p>
     * Throws <code>IllegalArgumentException</code> if the array is null.</p>
     * <p>
     * Does not change the internal state of the statistic.</p>
     *
     * @param values the input array
     * @param mean the precomputed mean value
     * @param begin index of the first array element to include
     * @param length the number of elements to include
     * @return the standard deviation of the values or Double.NaN if length = 0
     */
    public double evaluate(final double[] values, final double mean, final int begin,
                           final int length) {

        return FastMath.sqrt(variance.evaluate(values, mean, begin, length));
    }


    /**
     * Returns the Standard Deviation of the entries in the input array, using
     * the precomputed mean value.  Returns
     * <code>Double.NaN</code> if the designated subarray is empty.
     * <p>
     * Returns 0 for a single-value (i.e. length = 1) sample.</p>
     * <p>
     * The formula used assumes that the supplied mean value is the arithmetic
     * mean of the sample data, not a known population parameter.  This method
     * is supplied only to save computation when the mean has already been
     * computed.</p>
     * <p>
     * Throws <code>IllegalArgumentException</code> if the array is null.</p>
     * <p>
     * Does not change the internal state of the statistic.</p>
     *
     * @param values the input array
     * @param mean the precomputed mean value
     * @return the standard deviation of the values or Double.NaN if length = 0
     */
    public double evaluate(final double[] values, final double mean) {

        return FastMath.sqrt(variance.evaluate(values, mean));
    }


    /**
     * @return Returns the isBiasCorrected.
     */
    public boolean isBiasCorrected() {

        return variance.isBiasCorrected();
    }


    /**
     * @param isBiasCorrected The isBiasCorrected to set.
     */
    public void setBiasCorrected(boolean isBiasCorrected) {

        variance.setBiasCorrected(isBiasCorrected);
    }


    /**
     * {@inheritDoc}
     */
    @Override
    public StandardDeviation copy() {

        StandardDeviation result = new StandardDeviation();

        copy(this, result);

        return result;
    }


    /**
     * Copies source to dest.
     * <p>Neither source nor dest can be null.</p>
     *
     * @param source StandardDeviation to copy
     * @param dest StandardDeviation to copy to
     * @throws NullArgumentException if either source or dest is null
     */
    public static void copy(StandardDeviation source, StandardDeviation dest)
            throws NullArgumentException {

        MathUtils.checkNotNull(source);
        MathUtils.checkNotNull(dest);
        dest.setData(source.getDataRef());

        dest.variance = source.variance.copy();
    }
}
